Intelligent Time-Adaptive Transient Stability Assessment System

By James Jian Qiao Yu, David John Hill, Albert Y. S. Lam, Jiatao Gu, Victor O. K. Li. IEEE Transactions on Power Systems, 2016

337 citations recorded by the source corpus.

Online identification of postcontingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems. In this paper, we develop a transient stability assessment system based on the long short-term memory network. By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios. Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy. In addition, the model structure of our system is relatively less complex, speeding up the model training process. Case studies on three power systems demonstrate the efficacy of the proposed transient stability as sessment system.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7b60f5ff3793fcf526972e377bc1b2edfe4830c3

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Citations in the corpus

Foundations and Challenges of Low-Inertia Systems (Invited Paper) (2018, background): “In fact, there has been a recent line of work towards data-based stability assessment aimed at dealing with complexities like scale, time delays, and data loss among others [106]–[108].”
Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks (2019, methodology): “For cross validation and prevention of the over-ﬁtting problem, we randomly divide the generated fault and non-fault cases into training and testing sets by the ratio of 3:1, which accords with the common practice, see [4], [38].”
A Review of Machine Learning Approaches to Power System Security and Stability (2020, background): “As deﬁned by James et al. [36], transient stability is the ability of all the generators to preserve synchronism after a severe disturbance such as a fault or sudden loss of generator/ load or other components.”
Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks (2018, methodology): “For cross validation, the total 200 000 power dynamics test cases for each test system are randomly divided into a training set and a testing test by 3:1 ratio, which accords with the common practice [38].”